Overview

Dataset statistics

Number of variables11
Number of observations2241
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory192.7 KiB
Average record size in memory88.1 B

Variable types

DateTime1
Numeric10

Alerts

Open is highly correlated with High and 4 other fieldsHigh correlation
High is highly correlated with Open and 4 other fieldsHigh correlation
Low is highly correlated with Open and 4 other fieldsHigh correlation
Close is highly correlated with Open and 4 other fieldsHigh correlation
Adj Close is highly correlated with Open and 4 other fieldsHigh correlation
S_10 is highly correlated with Open and 4 other fieldsHigh correlation
Open-Close is highly correlated with Volume and 1 other fieldsHigh correlation
Open-Open is highly correlated with Volume and 1 other fieldsHigh correlation
Volume is highly correlated with Open-Close and 1 other fieldsHigh correlation
Date has unique values Unique
Corr has unique values Unique
Open-Close has 67 (3.0%) zeros Zeros

Reproduction

Analysis started2022-11-10 10:44:51.126247
Analysis finished2022-11-10 10:45:05.820639
Duration14.69 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Date
Date

UNIQUE

Distinct2241
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Minimum2013-12-04 00:00:00
Maximum2022-10-27 00:00:00
2022-11-10T16:15:05.879679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:05.984296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Open
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1715
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.97147479
Minimum13.94999981
Maximum78.36000061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2022-11-10T16:15:06.101319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum13.94999981
5-th percentile16.23999977
Q125.44000053
median35.27999878
Q344.20999908
95-th percentile63.97999954
Maximum78.36000061
Range64.4100008
Interquartile range (IQR)18.76999855

Descriptive statistics

Standard deviation14.16359889
Coefficient of variation (CV)0.3937452934
Kurtosis-0.2856451052
Mean35.97147479
Median Absolute Deviation (MAD)9.340000153
Skewness0.4720055958
Sum80612.075
Variance200.6075336
MonotonicityNot monotonic
2022-11-10T16:15:06.206561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
386
 
0.3%
36.56
 
0.3%
186
 
0.3%
36.450000765
 
0.2%
34.970001225
 
0.2%
16.610000615
 
0.2%
17.959999084
 
0.2%
43.54
 
0.2%
364
 
0.2%
16.649999624
 
0.2%
Other values (1705)2192
97.8%
ValueCountFrequency (%)
13.949999811
< 0.1%
14.060000421
< 0.1%
14.069999692
0.1%
14.090000151
< 0.1%
14.119999891
< 0.1%
14.149999621
< 0.1%
14.170000081
< 0.1%
14.199999811
< 0.1%
14.210000041
< 0.1%
14.220000272
0.1%
ValueCountFrequency (%)
78.360000611
< 0.1%
78.150001531
< 0.1%
76.870002751
< 0.1%
76.610000611
< 0.1%
74.010002141
< 0.1%
73.099998471
< 0.1%
73.050003051
< 0.1%
72.970001221
< 0.1%
72.879997251
< 0.1%
72.510002141
< 0.1%

High
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1833
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.64737637
Minimum14.22000027
Maximum80.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2022-11-10T16:15:06.319305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14.22000027
5-th percentile16.51009941
Q126.05999947
median35.97000122
Q345.02999878
95-th percentile65.05000305
Maximum80.75
Range66.52999973
Interquartile range (IQR)18.96999931

Descriptive statistics

Standard deviation14.41615268
Coefficient of variation (CV)0.3933747543
Kurtosis-0.2791660898
Mean36.64737637
Median Absolute Deviation (MAD)9.36000061
Skewness0.4711391613
Sum82126.77045
Variance207.825458
MonotonicityNot monotonic
2022-11-10T16:15:06.425828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.069999695
 
0.2%
424
 
0.2%
17.049999244
 
0.2%
34.990001684
 
0.2%
17.600000384
 
0.2%
41.479999544
 
0.2%
17.149999623
 
0.1%
40.349998473
 
0.1%
17.950000763
 
0.1%
31.729999543
 
0.1%
Other values (1823)2204
98.3%
ValueCountFrequency (%)
14.220000271
< 0.1%
14.251
< 0.1%
14.279999731
< 0.1%
14.305000311
< 0.1%
14.369999891
< 0.1%
14.399999622
0.1%
14.430000311
< 0.1%
14.449999811
< 0.1%
14.460000041
< 0.1%
14.470000271
< 0.1%
ValueCountFrequency (%)
80.751
< 0.1%
79.080001831
< 0.1%
78.730003361
< 0.1%
77.099998471
< 0.1%
74.959999081
< 0.1%
74.839996341
< 0.1%
74.730003361
< 0.1%
74.51
< 0.1%
74.330001831
< 0.1%
73.949996951
< 0.1%

Low
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1780
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.29535744
Minimum13.72500038
Maximum76.05000305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2022-11-10T16:15:06.538510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum13.72500038
5-th percentile16
Q124.70000076
median34.72999954
Q343.38000107
95-th percentile62.75310135
Maximum76.05000305
Range62.32500267
Interquartile range (IQR)18.68000031

Descriptive statistics

Standard deviation13.87487359
Coefficient of variation (CV)0.3931076097
Kurtosis-0.3112718315
Mean35.29535744
Median Absolute Deviation (MAD)9.11000061
Skewness0.4651023253
Sum79096.89601
Variance192.5121173
MonotonicityNot monotonic
2022-11-10T16:15:06.641239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.430000315
 
0.2%
16.010000235
 
0.2%
17.469999314
 
0.2%
36.409999854
 
0.2%
28.930000314
 
0.2%
17.520000464
 
0.2%
15.960000044
 
0.2%
28.430000314
 
0.2%
16.459999084
 
0.2%
17.54
 
0.2%
Other values (1770)2199
98.1%
ValueCountFrequency (%)
13.725000381
 
< 0.1%
13.899999621
 
< 0.1%
13.909999851
 
< 0.1%
13.920000081
 
< 0.1%
13.979999541
 
< 0.1%
143
0.1%
14.039999961
 
< 0.1%
14.050000191
 
< 0.1%
14.060000422
0.1%
14.079999921
 
< 0.1%
ValueCountFrequency (%)
76.050003051
< 0.1%
751
< 0.1%
73.889999391
< 0.1%
73.559997561
< 0.1%
71.879997251
< 0.1%
71.813499451
< 0.1%
71.709999081
< 0.1%
70.730003361
< 0.1%
70.658699041
< 0.1%
70.419998171
< 0.1%

Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1728
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.95658633
Minimum14.01000023
Maximum77.62999725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2022-11-10T16:15:06.753525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14.01000023
5-th percentile16.19000053
Q125.29000092
median35.36999893
Q344.15000153
95-th percentile63.83000183
Maximum77.62999725
Range63.61999702
Interquartile range (IQR)18.86000061

Descriptive statistics

Standard deviation14.13605727
Coefficient of variation (CV)0.3931423617
Kurtosis-0.3010165699
Mean35.95658633
Median Absolute Deviation (MAD)9.340000153
Skewness0.4659142626
Sum80578.70997
Variance199.8281151
MonotonicityNot monotonic
2022-11-10T16:15:06.861006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.299999245
 
0.2%
18.629999165
 
0.2%
16.909999854
 
0.2%
17.610000614
 
0.2%
38.790000924
 
0.2%
14.399999624
 
0.2%
36.849998474
 
0.2%
16.610000614
 
0.2%
17.090000154
 
0.2%
32.729999544
 
0.2%
Other values (1718)2199
98.1%
ValueCountFrequency (%)
14.010000231
< 0.1%
14.020000461
< 0.1%
14.029999731
< 0.1%
14.079999921
< 0.1%
14.100000381
< 0.1%
14.119999891
< 0.1%
14.140000341
< 0.1%
14.149999621
< 0.1%
14.199999811
< 0.1%
14.289999962
0.1%
ValueCountFrequency (%)
77.629997251
< 0.1%
77.059997561
< 0.1%
74.589996341
< 0.1%
73.959999081
< 0.1%
73.669998171
< 0.1%
73.309997561
< 0.1%
73.169998171
< 0.1%
72.449996951
< 0.1%
72.279998781
< 0.1%
72.260002141
< 0.1%

Adj Close
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1728
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.95658633
Minimum14.01000023
Maximum77.62999725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2022-11-10T16:15:06.975601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14.01000023
5-th percentile16.19000053
Q125.29000092
median35.36999893
Q344.15000153
95-th percentile63.83000183
Maximum77.62999725
Range63.61999702
Interquartile range (IQR)18.86000061

Descriptive statistics

Standard deviation14.13605727
Coefficient of variation (CV)0.3931423617
Kurtosis-0.3010165699
Mean35.95658633
Median Absolute Deviation (MAD)9.340000153
Skewness0.4659142626
Sum80578.70997
Variance199.8281151
MonotonicityNot monotonic
2022-11-10T16:15:07.082168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.299999245
 
0.2%
18.629999165
 
0.2%
16.909999854
 
0.2%
17.610000614
 
0.2%
38.790000924
 
0.2%
14.399999624
 
0.2%
36.849998474
 
0.2%
16.610000614
 
0.2%
17.090000154
 
0.2%
32.729999544
 
0.2%
Other values (1718)2199
98.1%
ValueCountFrequency (%)
14.010000231
< 0.1%
14.020000461
< 0.1%
14.029999731
< 0.1%
14.079999921
< 0.1%
14.100000381
< 0.1%
14.119999891
< 0.1%
14.140000341
< 0.1%
14.149999621
< 0.1%
14.199999811
< 0.1%
14.289999962
0.1%
ValueCountFrequency (%)
77.629997251
< 0.1%
77.059997561
< 0.1%
74.589996341
< 0.1%
73.959999081
< 0.1%
73.669998171
< 0.1%
73.309997561
< 0.1%
73.169998171
< 0.1%
72.449996951
< 0.1%
72.279998781
< 0.1%
72.260002141
< 0.1%

Volume
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2240
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21800563.62
Minimum0
Maximum269213085
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2022-11-10T16:15:07.198164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7925508
Q112416087
median17009916
Q324292612
95-th percentile49952021
Maximum269213085
Range269213085
Interquartile range (IQR)11876525

Descriptive statistics

Standard deviation19033730.19
Coefficient of variation (CV)0.8730843167
Kurtosis41.90241477
Mean21800563.62
Median Absolute Deviation (MAD)5529517
Skewness5.140502278
Sum4.885506307 × 1010
Variance3.62282885 × 1014
MonotonicityNot monotonic
2022-11-10T16:15:07.310699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
0.1%
110289531
 
< 0.1%
121705981
 
< 0.1%
227405981
 
< 0.1%
106244481
 
< 0.1%
96931711
 
< 0.1%
151441501
 
< 0.1%
93163961
 
< 0.1%
137146941
 
< 0.1%
145375561
 
< 0.1%
Other values (2230)2230
99.5%
ValueCountFrequency (%)
02
0.1%
36610531
< 0.1%
41799671
< 0.1%
42904601
< 0.1%
48322611
< 0.1%
48702581
< 0.1%
50613711
< 0.1%
51333461
< 0.1%
52444031
< 0.1%
53295081
< 0.1%
ValueCountFrequency (%)
2692130851
< 0.1%
2588683391
< 0.1%
2175200981
< 0.1%
1922692551
< 0.1%
1768036351
< 0.1%
1624343601
< 0.1%
1590347471
< 0.1%
1531195501
< 0.1%
1478057101
< 0.1%
1363451281
< 0.1%

S_10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2223
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.93349798
Minimum14.23800001
Maximum73.51499863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2022-11-10T16:15:07.421453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14.23800001
5-th percentile16.32999973
Q125.53100014
median35.38999977
Q344.01299973
95-th percentile63.45400085
Maximum73.51499863
Range59.27699862
Interquartile range (IQR)18.48199959

Descriptive statistics

Standard deviation14.01391481
Coefficient of variation (CV)0.3899958424
Kurtosis-0.3425124407
Mean35.93349798
Median Absolute Deviation (MAD)9.057999611
Skewness0.4464252222
Sum80526.96897
Variance196.3898084
MonotonicityNot monotonic
2022-11-10T16:15:07.526036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.274000173
 
0.1%
40.529999922
 
0.1%
44.69099962
 
0.1%
16.119999892
 
0.1%
18.060000042
 
0.1%
25.735000042
 
0.1%
24.779000092
 
0.1%
41.10799982
 
0.1%
36.650999832
 
0.1%
44.588999942
 
0.1%
Other values (2213)2220
99.1%
ValueCountFrequency (%)
14.238000011
< 0.1%
14.256000041
< 0.1%
14.260000041
< 0.1%
14.289000031
< 0.1%
14.292000011
< 0.1%
14.295000081
< 0.1%
14.316000081
< 0.1%
14.326000021
< 0.1%
14.336999991
< 0.1%
14.359000021
< 0.1%
ValueCountFrequency (%)
73.514998631
< 0.1%
73.485998541
< 0.1%
73.39299851
< 0.1%
72.941999051
< 0.1%
72.841998291
< 0.1%
72.308998111
< 0.1%
72.091999051
< 0.1%
71.607998281
< 0.1%
71.409999081
< 0.1%
71.042998121
< 0.1%

Corr
Real number (ℝ)

UNIQUE

Distinct2241
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3655161423
Minimum-0.9186919345
Maximum0.9966978801
Zeros0
Zeros (%)0.0%
Negative566
Negative (%)25.3%
Memory size17.6 KiB
2022-11-10T16:15:07.657239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.9186919345
5-th percentile-0.5894422128
Q1-0.006494486879
median0.5060910778
Q30.787934874
95-th percentile0.9432323624
Maximum0.9966978801
Range1.915389815
Interquartile range (IQR)0.7944293609

Descriptive statistics

Standard deviation0.4968815939
Coefficient of variation (CV)1.359397127
Kurtosis-0.6520742017
Mean0.3655161423
Median Absolute Deviation (MAD)0.3378259446
Skewness-0.6998809454
Sum819.1216749
Variance0.2468913183
MonotonicityNot monotonic
2022-11-10T16:15:07.807440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.22335588551
 
< 0.1%
0.77197947111
 
< 0.1%
0.79110576471
 
< 0.1%
0.75048433361
 
< 0.1%
0.72260652511
 
< 0.1%
0.70704866821
 
< 0.1%
0.76065696621
 
< 0.1%
0.79983722571
 
< 0.1%
0.91313972291
 
< 0.1%
0.69787463051
 
< 0.1%
Other values (2231)2231
99.6%
ValueCountFrequency (%)
-0.91869193451
< 0.1%
-0.91372897251
< 0.1%
-0.89131067891
< 0.1%
-0.88781710431
< 0.1%
-0.88710880391
< 0.1%
-0.88517197191
< 0.1%
-0.88402067831
< 0.1%
-0.88355283511
< 0.1%
-0.87763084661
< 0.1%
-0.87065275181
< 0.1%
ValueCountFrequency (%)
0.99669788011
< 0.1%
0.98693518141
< 0.1%
0.98685251311
< 0.1%
0.9858792481
< 0.1%
0.98540406091
< 0.1%
0.98508331
< 0.1%
0.98342042321
< 0.1%
0.98340406261
< 0.1%
0.9823034951
< 0.1%
0.97831229411
< 0.1%

Open-Close
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct623
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02039046247
Minimum-15.36000061
Maximum8.559997559
Zeros67
Zeros (%)3.0%
Negative954
Negative (%)42.6%
Memory size17.6 KiB
2022-11-10T16:15:07.948272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-15.36000061
5-th percentile-0.8400001526
Q1-0.1599998474
median0.0299987793
Q30.2300014496
95-th percentile0.8950004578
Maximum8.559997559
Range23.91999817
Interquartile range (IQR)0.390001297

Descriptive statistics

Standard deviation0.8668237054
Coefficient of variation (CV)42.51123321
Kurtosis71.85074499
Mean0.02039046247
Median Absolute Deviation (MAD)0.1999969482
Skewness-2.63770438
Sum45.6950264
Variance0.7513833363
MonotonicityNot monotonic
2022-11-10T16:15:08.085677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
067
 
3.0%
0.0200004577633
 
1.5%
0.0900001525929
 
1.3%
-0.0100002288827
 
1.2%
0.0100002288826
 
1.2%
0.0699996948224
 
1.1%
-0.0200004577623
 
1.0%
-0.159999847423
 
1.0%
-0.0699996948222
 
1.0%
0.180000305221
 
0.9%
Other values (613)1946
86.8%
ValueCountFrequency (%)
-15.360000611
< 0.1%
-9.0899963381
< 0.1%
-7.9000015261
< 0.1%
-6.9700012211
< 0.1%
-6.3100013731
< 0.1%
-5.6899986271
< 0.1%
-4.9599990841
< 0.1%
-4.6800003051
< 0.1%
-4.4100017551
< 0.1%
-4.389999391
< 0.1%
ValueCountFrequency (%)
8.5599975591
< 0.1%
8.4199981691
< 0.1%
7.2400016781
< 0.1%
6.2300033571
< 0.1%
5.930004121
< 0.1%
4.860000611
< 0.1%
4.8100013731
< 0.1%
4.5299987791
< 0.1%
3.8799972531
< 0.1%
3.7799987791
< 0.1%

Open-Open
Real number (ℝ)

HIGH CORRELATION

Distinct936
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005899152709
Minimum-16.55000305
Maximum8.899997711
Zeros17
Zeros (%)0.8%
Negative1073
Negative (%)47.9%
Memory size17.6 KiB
2022-11-10T16:15:08.205644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-16.55000305
5-th percentile-1.920001984
Q1-0.4599990845
median0.0299987793
Q30.5200004578
95-th percentile1.800003052
Maximum8.899997711
Range25.45000076
Interquartile range (IQR)0.9799995422

Descriptive statistics

Standard deviation1.355142258
Coefficient of variation (CV)229.7181181
Kurtosis21.60342578
Mean0.005899152709
Median Absolute Deviation (MAD)0.4900016785
Skewness-1.379261603
Sum13.22000122
Variance1.83641054
MonotonicityNot monotonic
2022-11-10T16:15:08.334648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017
 
0.8%
0.0699996948215
 
0.7%
0.450000762915
 
0.7%
-0.409999847414
 
0.6%
-0.200000762913
 
0.6%
0.180000305213
 
0.6%
-0.0900001525913
 
0.6%
0.430000305213
 
0.6%
0.569999694812
 
0.5%
-0.0200004577612
 
0.5%
Other values (926)2104
93.9%
ValueCountFrequency (%)
-16.550003051
< 0.1%
-11.950000761
< 0.1%
-11.010002141
< 0.1%
-9.8299980161
< 0.1%
-8.9599990841
< 0.1%
-7.7700004581
< 0.1%
-7.4200019841
< 0.1%
-7.1300010681
< 0.1%
-6.3700027471
< 0.1%
-5.6199989321
< 0.1%
ValueCountFrequency (%)
8.8999977111
< 0.1%
8.7099990841
< 0.1%
8.3200016021
< 0.1%
7.4499969481
< 0.1%
7.1600036621
< 0.1%
6.5400009161
< 0.1%
6.4899978641
< 0.1%
5.9799995421
< 0.1%
5.2400016781
< 0.1%
5.0500030521
< 0.1%

Interactions

2022-11-10T16:15:04.108048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:54.610107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.493293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.373230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.366051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:58.802402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.036498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.059515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:02.013189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.098594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:04.200225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:54.703437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.579418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.457627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.471275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:58.931067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.165565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.183775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:02.106220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.184820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:04.297456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:54.791527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.670620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.544728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.579016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:59.054726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.288279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.319971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:02.200613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.299336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:04.387274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:54.878111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.756332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.629031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.669315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:59.168423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.406636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.407328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:02.293307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.428293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:04.487082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:54.968359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.847395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.719327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.763064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:59.299080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.518385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.494684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:02.386701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.533391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:04.630705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.056669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.936771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.808678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.855860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:59.422576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.609907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.580450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:02.480162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.638563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:04.753889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.144014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.023919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.921185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.949607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:59.544251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.703002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.665850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:02.599135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.730853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:05.288136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.231832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.108137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.033751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:58.038152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:59.663932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.791265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.750163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:02.746742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.818112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:05.378960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.327515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.205394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.160076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:58.558513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:59.799618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.888927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.845026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:02.908915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.922292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:05.460873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:55.407967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:56.289939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:57.261760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:58.673746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:14:59.913431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:00.971393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:01.928607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:03.006995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-10T16:15:04.015497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-10T16:15:08.442468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-10T16:15:08.643928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-10T16:15:08.806491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-10T16:15:08.934390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-10T16:15:09.059847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-10T16:15:05.597249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-10T16:15:05.760795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateOpenHighLowCloseAdj CloseVolumeS_10CorrOpen-CloseOpen-Open
02013-12-0441.27000043.91999841.27000043.68999943.6899991102895341.166-0.223356-0.0999980.580002
12013-12-0543.45000146.34999842.83000245.61999945.6199991181352041.623-0.143194-0.2399982.180000
22013-12-0645.75000045.79999944.54000144.95000144.950001623623241.912-0.0414330.1300012.299999
32013-12-0945.59000049.84000045.02000049.13999949.1399991736661442.7260.5709990.639999-0.160000
42013-12-1048.90000252.58000248.70000151.99000251.9900022579200244.0190.881595-0.2399983.310001
52013-12-1152.40000253.86999951.00000052.34000052.3400002663153545.2350.9275080.4100003.500000
62013-12-1252.20000155.86999950.68999955.33000255.3300022344687046.6780.956154-0.139999-0.200001
72013-12-1356.20000159.41000055.45000159.00000059.0000003897956748.4210.9714900.8699994.000000
82013-12-1657.86000160.24000255.75999856.61000156.6100013931084850.0040.943232-1.1399991.660000
92013-12-1756.97000157.38000154.61999956.45000156.4500012211519951.5120.9121750.360001-0.889999

Last rows

DateOpenHighLowCloseAdj CloseVolumeS_10CorrOpen-CloseOpen-Open
22312022-10-1450.49000250.86500250.29999950.45000150.4500011007590849.5570.4281760.1500020.690002
22322022-10-1750.50000050.90000250.20000150.74000250.7400021410127050.377-0.2564700.0499990.009998
22332022-10-1851.09999852.09999850.84999851.77999951.7799991853390750.3550.4095490.3599970.599998
22342022-10-1951.79999952.16000051.25999851.83000251.8300021005792850.4080.8566740.0200000.700001
22352022-10-2052.20000152.70000151.59999852.43999952.4399992546101950.7130.8421150.3699990.400002
22362022-10-2150.00000050.75000049.54999949.88999949.8899995120902950.7840.557250-2.439999-2.200001
22372022-10-2450.70999951.86000150.52000051.52000051.5200002298755350.9000.6233560.8200000.709999
22382022-10-2552.41500153.18000052.20000152.77999952.7799993507784851.1710.6578340.8950001.705002
22392022-10-2652.95000153.50000052.77000053.34999853.3499982806497351.5120.6819790.1700020.535000
22402022-10-2753.91000054.00000053.70000153.70000153.70000113634512851.8480.7573570.5600010.959999